1. Field of Invention
The techniques described herein are directed generally to the field of input classification, and more particularly to techniques for performing evaluation, building and/or retraining of a classification model.
2. Description of the Related Art
Classification is the process of determining a class for a given data input. For example, binary classification may be used to classify a data input into one of two classes. One environment in which classification is performed is in connection with an automatic speech recognition (ASR) system that processes speech and may, for each segment of speech, provide output for a word or phrase that the ASR system has determined is a representation of the speech. Binary classification may be used to determine whether each ASR output belongs in the ‘accept’ class, or the ‘reject’ class, wherein the class is an indication of whether the ASR output is to be accepted as correct or rejected as incorrect, respectively.
A classification model is a statistical model constructed with the aim of correctly associating a given data input with a class. A classification model may be constructed using supervised training, in which inputs with labels identifying their known classes are used to train the classification model. The classification model is thereby able to learn how to correctly assign classes based on the labeled training data, and may then be used to determine the classes of unlabeled input for which the class is unknown.